Rough Set

... Krusinska, E., Slowinski, R. and Stefanowski, J., 1992. Discriminant versus rough set approach to vague data analysis. Applied Stochastic Models and Data Analysis 8, pp. ... Lin, T., 1989.Neighborhood systems and approximation in database and knowledge base systems. ...

Intrusion detection system (IDS) is an important tool for the defense of a network against attacks. It monitors the activities occurring in a computer system or network and analyzes them for recognizing intrusions to protect the computer network. Most of the existing IDSs use all of the 41 features available in the network packet to analyze and look for intrusive

This paper investigates the method of forecasting stock price difference on artificially generated price series data using neuro-fuzzy systems and neural networks. As trading profits is more important to an investor than statistical performance, this paper proposes a novel rough set-based neuro-fuzzy stock trading decision model called stock trading using rough set-based pseudo outer-product (RSPOP) which synergizes the price difference forecast method with a forecast bottleneck free trading decision model. The proposed stock trading with forecast model uses the pseudo outer-product based fuzzy neural network using the compositional rule of inference [POPFNN-CRI(S)] with fuzzy rules identified using the RSPOP algorithm as the underlying predictor model and simple moving average trading rules in the stock trading decision model. Experimental results using the proposed stock trading with RSPOP forecast model on real world stock market data are presented. Trading profits in terms of portfolio end values obtained are benchmarked against stock trading with dynamic evolving neural-fuzzy inference system (DENFIS) forecast model, the stock trading without forecast model and the stock trading with ideal forecast model. Experimental results showed that the proposed model identified rules with greater interpretability and yielded significantly higher profits than the stock trading with DENFIS forecast model and the stock trading without forecast model

This paper presents a review of the current liter- ature on rough set and near set-based approaches to solving various problems in medical imaging such as medical image segmentation, object extraction and image classification. Rough set frameworks hybridized with other computational intelligence technologies that include neural networks, particle swarm op- timization, support vector machines and fuzzy sets are also presented.

Regulatory reform in EU transport policy has prompted urban public transport authorities to operate increasingly under a competitive market regime. The EU policy favours in particular a system of limited competition through the granting of concessions to public transport operators. This paper reviews the emerging organisational framework and seeks to identify the success conditions for local public transport systems in a sample of 22 European cities. On the basis of extensive field research, a systematic performance table of urban public transport systems in these cities is created with the aim of investigating the impact of four classes of critical success factors on the performance of these systems. In the empirical part both a qualitative interpretative analysis and a recently developed tool from artificial intelligence, viz. rough set analysis, is deployed in order to derive policy relevant conclusions.

... Ordered Belief Fusion in Possibilistic Logic Churn-Jung Liau ..... ... Toward More Reliable Emotion Recognition of Vocal Sentences by Emphasizing Information of Korean Ending Boundary Tones Tae-Seung Lee, Mikyoung Park, Tae-Soo Kim ..... ...

The automated generation of feature pattern-based if-then rules is essential to the success of many intelligent pattern classifiers, especially when their inference results are expected to be directly human-comprehensible. Fuzzy and rough set theory have been applied with much success to this area as well as to feature selection. Since both applications of rough set theory involve the processing of equivalence classes for their successful operation, it is natural to combine them into a single integrated method that generates concise, meaningful and accurate rules. This paper proposes such an approach, based on fuzzy-rough sets. The algorithm is experimentally evaluated against leading classifiers, including fuzzy and rough rule inducers, and shown to be effective.